Merge remote-tracking branch 'origin/main' into docs/stale-migration
# Conflicts: # docs/migration/api-changes.mdx
This commit is contained in:
kartik-mem0
@@ -50,6 +50,7 @@ Provide conversation messages for Mem0 to extract memories from. At least one en
|
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| `app_id` | string | No* | Associates the memory with an app. |
|
||||
| `metadata` | object | Optional | Custom key/value metadata (e.g., `{"topic": "preferences"}`). |
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| `infer` | boolean (default `true`) | Optional | Set to `false` to skip inference and store the provided text as-is. |
|
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| `expiration_date` | string | Optional | Date in `YYYY-MM-DD` format. The memory is visible through this date and hidden by default after it passes. |
|
||||
|
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> \* At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required.
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|
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@@ -83,3 +84,11 @@ The request is queued for background processing. The response contains an `event
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<Info>
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Poll the event status via `GET /v1/event/{event_id}/`. Status will be `SUCCEEDED` or `FAILED` once processing completes.
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</Info>
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||||
|
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<Info>
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Memories with `expiration_date` remain stored after they expire. Search and get-all hide them by default; pass `show_expired: true` to include them.
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</Info>
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|
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<Info>
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Python uses `expiration_date`; TypeScript uses `expirationDate`.
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</Info>
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@@ -4,4 +4,4 @@ description: "Submit an export job to create a structured memory export using a
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openapi: post /v1/exports/
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---
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Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `run_id`, or `session_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
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Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `app_id`, or `run_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
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@@ -6,6 +6,10 @@ openapi: post /v3/memories/
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List memories scoped by filters with paginated results. Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400.
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Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
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Python uses `show_expired`; TypeScript uses `showExpired`.
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The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
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- `in`: Matches any of the values specified
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@@ -32,6 +36,7 @@ memories = client.get_all(
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}
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]
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},
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show_expired=False,
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page=1,
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page_size=50
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)
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@@ -46,12 +51,14 @@ memories = client.get_all(
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{
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"id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
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"memory": "Alex is planning a trip to San Francisco from July 1st to July 10th",
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"expiration_date": null,
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"created_at": "2024-07-01T12:00:00Z",
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"updated_at": "2024-07-01T12:00:00Z"
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},
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{
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"id": "a2b8c3d4-5e6f-7g8h-9i0j-1k2l3m4n5o6p",
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"memory": "Alex prefers vegetarian restaurants",
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"expiration_date": null,
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"created_at": "2024-07-05T15:30:00Z",
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"updated_at": "2024-07-05T15:30:00Z"
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}
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@@ -4,4 +4,4 @@ description: "Retrieve the latest structured memory export after submitting an e
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openapi: post /v1/exports/get
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---
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Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.
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Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `agent_id`, `app_id`, `run_id`, `created_at`, or `updated_at` to get the most recent export matching your filters.
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@@ -8,6 +8,10 @@ Relevance-ranked hybrid search across stored memories. V3 uses multi-signal retr
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Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400. At least one entity ID is required.
|
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|
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Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
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|
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Python uses `show_expired`; TypeScript uses `showExpired`.
|
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|
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The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
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- `in`: Matches any of the values specified
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- `gte`: Greater than or equal to
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@@ -20,16 +24,17 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
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### Search parameter defaults
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| Parameter | V1/V2 | V3 |
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| --- | --- | --- |
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| `top_k` | Supported (default 10) | Supported (1-1000, default 10) |
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| `threshold` | No default | Default `0.1` (pass `0.0` to disable) |
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| `rerank` | Default `true` | Default `false` (pass `true` to enable) |
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| Parameter | Default |
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| --- | --- |
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| `top_k` | `10` (range 1–1000) |
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| `threshold` | `0.1` (pass `0.0` to disable) |
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| `rerank` | `false` (pass `true` to enable) |
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<CodeGroup>
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```python Platform API Example
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related_memories = client.search(
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query="What are Alice's hobbies?",
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show_expired=False,
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filters={
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"OR": [
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{
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@@ -54,6 +59,7 @@ related_memories = client.search(
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"category": "hobbies"
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},
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"score": 0.82,
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"expiration_date": null,
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"created_at": "2024-07-26T10:29:36.630547-07:00",
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"updated_at": null,
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"categories": ["hobbies"]
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@@ -1,5 +1,14 @@
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---
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title: 'Update Memory'
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description: "Update the content or metadata of a single memory by its unique ID using the PUT endpoint."
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description: "Update the content, metadata, timestamp, or expiration date of a single memory by its unique ID using the PUT endpoint."
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openapi: put /v1/memories/{memory_id}/
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---
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---
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Use this endpoint to update mutable memory fields. To make a memory expire, set `expiration_date` to a `YYYY-MM-DD` date. To make it permanent again, send `expiration_date: null`.
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```python
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client.update("mem_123", expiration_date="2030-01-31")
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client.update("mem_123", expiration_date=None)
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```
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TypeScript uses `expirationDate`.
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@@ -0,0 +1,5 @@
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---
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title: "Remove Organization Member"
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description: "Remove a member from an organization to revoke their access to its projects and resources."
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openapi: "delete /api/v1/orgs/organizations/{org_id}/members/"
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---
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@@ -0,0 +1,5 @@
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---
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title: "Update Organization Member"
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description: "Update an existing member's role within an organization to change their permissions and access level."
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openapi: "put /api/v1/orgs/organizations/{org_id}/members/"
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---
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@@ -14,7 +14,7 @@ Organizations and projects are **optional** features. You can use Mem0 without t
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## Key Capabilities
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- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
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- **Multi-org/project Support**: Organization and project are resolved automatically from your API key via `/v1/ping/` — no org or project params are accepted by `MemoryClient.__init__`. Use a project-specific API key to target a particular project.
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- **Member Management**: Control access to data through organization and project membership
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- **Access Control**: Only members can access memories and data within their organization/project scope
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- **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
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@@ -79,7 +79,7 @@ new_project = client.project.create(
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### Update Project Settings
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Modify project configuration including custom instructions, categories, and language preferences:
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Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:
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```python
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# Update project with custom categories
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@@ -98,6 +98,17 @@ client.project.update(
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# Use the input language for memory storage and retrieval
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client.project.update(multilingual=True)
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# Set retrieval criteria to control which memories are surfaced in search
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client.project.update(
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retrieval_criteria=[
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{"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
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{"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
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]
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)
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# Enable Memory Decay (boosts recently-accessed memories at search time)
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client.project.update(decay=True)
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# Update multiple settings at once
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client.project.update(
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custom_instructions="...",
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@@ -109,6 +120,34 @@ client.project.update(
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)
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```
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#### Set Retrieval Criteria
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`retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
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||||
```python
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client.project.update(
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retrieval_criteria=[
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{
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"name": "joy",
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"description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
|
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"weight": 3
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||||
},
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{
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"name": "curiosity",
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"description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
|
||||
"weight": 2
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||||
},
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||||
{
|
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"name": "access_frequency",
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"description": "How often this memory has been accessed or surfaced recently.",
|
||||
"weight": 1
|
||||
}
|
||||
]
|
||||
)
|
||||
```
|
||||
|
||||
Pass an empty list to clear all criteria and restore default retrieval behaviour.
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||||
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||||
#### Toggle Memory Decay
|
||||
|
||||
`decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay) — a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:
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|
||||
@@ -0,0 +1,5 @@
|
||||
---
|
||||
title: "Remove Project Member"
|
||||
description: "Remove a member from a project to revoke their access to its memories, configuration, and resources."
|
||||
openapi: "delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
|
||||
---
|
||||
@@ -0,0 +1,5 @@
|
||||
---
|
||||
title: "Update Project Member"
|
||||
description: "Update an existing member's role within a project to change their permissions and access level."
|
||||
openapi: "put /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
|
||||
---
|
||||
@@ -0,0 +1,5 @@
|
||||
---
|
||||
title: "Update Project"
|
||||
description: "Update a project's settings, including name, custom instructions, and other configuration options."
|
||||
openapi: "patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/"
|
||||
---
|
||||
@@ -113,7 +113,7 @@ Launched a unified Mem0 plugin across three major AI development environments
|
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|
||||
Major expansion of the provider ecosystem:
|
||||
|
||||
- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE)
|
||||
- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE). **Note:** All external graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE) were subsequently removed in v2.0.0 (2026-04-14). Graph memory is now built-in entity linking with no external graph store required; see the [v2.0.0 entry above](#mem0-sdk-v2-0-0-v3-0-0).
|
||||
- **Turbopuffer** — New vector database provider for Python SDK
|
||||
- **MiniMax** — New LLM provider with dedicated AWS Bedrock support
|
||||
- **pgvector for Node.js** — PostgreSQL vector support added to the TypeScript OSS SDK
|
||||
|
||||
@@ -121,7 +121,7 @@ mode: "wide"
|
||||
|
||||
**New Features:**
|
||||
- **Memory:** Warn at init time when hybrid/BM25 search silently degrades to semantic-only because the configured vector store does not implement `keyword_search`. Affected stores: Chroma, FAISS, Cassandra, LangChain, Neptune Analytics, S3 Vectors, Supabase, TurboPuffer, Valkey ([#5444](https://github.com/mem0ai/mem0/pull/5444))
|
||||
- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_breakdown` dict with `semantic`, `keyword` (normalized BM25), `entity_boost`, and `temporal_boost` signals so callers can understand and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
|
||||
- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_details` dict with `semantic_score`, `bm25_score`, `entity_boost`, `raw_score`, `max_possible_score`, `final_score`, and `threshold` so callers can understand and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
|
||||
|
||||
**Bug Fixes:**
|
||||
- **Vector Stores:** Normalize similarity scores to `[0, 1]` (higher = better) consistently across all backends. 11 adapters previously returned raw distance metrics (lower = better) — FAISS, Chroma, Milvus, Redis, Cassandra, PGVector, S3 Vectors, Supabase, Valkey, Azure MySQL, and Vertex AI Vector Search — causing incorrect ranking in multi-store setups ([#5391](https://github.com/mem0ai/mem0/pull/5391))
|
||||
@@ -229,7 +229,7 @@ mode: "wide"
|
||||
**Improvements:**
|
||||
- **Telemetry:** Sample OSS hot-path events at 10% via PostHog `before_send` hook to reduce event volume ([#4771](https://github.com/mem0ai/mem0/pull/4771))
|
||||
|
||||
See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-v2) and [Platform migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for upgrade instructions.
|
||||
See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) and [Platform migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for upgrade instructions.
|
||||
|
||||
</Update>
|
||||
|
||||
|
||||
@@ -0,0 +1,50 @@
|
||||
---
|
||||
title: "FastEmbed"
|
||||
description: "Configure FastEmbed as an embedding provider in Mem0 to generate embeddings locally using ONNX-based models without a GPU."
|
||||
---
|
||||
|
||||
You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an ONNX-based embedding library that runs efficiently on CPU without requiring a GPU or an external API key.
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
pip install fastembed
|
||||
```
|
||||
|
||||
### Usage
|
||||
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
import os
|
||||
from mem0 import Memory
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
|
||||
|
||||
config = {
|
||||
"embedder": {
|
||||
"provider": "fastembed",
|
||||
"config": {
|
||||
"model": "thenlper/gte-large"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
m = Memory.from_config(config)
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
]
|
||||
m.add(messages, user_id="john")
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
### Config
|
||||
|
||||
Here are the parameters available for configuring FastEmbed embedder:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `model` | The name of the FastEmbed model to use | `thenlper/gte-large` |
|
||||
| `embedding_dims` | Dimensions of the embedding model (auto-derived from the model if not set) | `None` |
|
||||
@@ -4,9 +4,12 @@ description: "Use LiteLLM as an LLM provider in Mem0 to access over 100 language
|
||||
---
|
||||
[Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.
|
||||
|
||||
In the TypeScript SDK, run LiteLLM as a [proxy server](https://docs.litellm.ai/docs/simple_proxy) (an OpenAI-compatible endpoint) and point Mem0 at it via `LITELLM_API_BASE` (defaults to `http://localhost:4000`).
|
||||
|
||||
## Usage
|
||||
|
||||
```python
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
import os
|
||||
from mem0 import Memory
|
||||
|
||||
@@ -33,6 +36,33 @@ messages = [
|
||||
m.add(messages, user_id="alice", metadata={"category": "movies"})
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
|
||||
// Point Mem0 at your LiteLLM proxy. apiKey defaults to "sk-anything"
|
||||
// (the proxy handles real auth); baseURL defaults to http://localhost:4000.
|
||||
const config = {
|
||||
llm: {
|
||||
provider: 'litellm',
|
||||
config: {
|
||||
apiKey: process.env.LITELLM_API_KEY || 'sk-anything',
|
||||
baseURL: process.env.LITELLM_API_BASE || 'http://localhost:4000',
|
||||
model: 'gpt-5-mini',
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
const memory = new Memory(config);
|
||||
const messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
];
|
||||
await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
## Config
|
||||
|
||||
All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).
|
||||
@@ -7,7 +7,8 @@ To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment var
|
||||
|
||||
## Usage
|
||||
|
||||
```python
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
import os
|
||||
from mem0 import Memory
|
||||
|
||||
@@ -36,9 +37,37 @@ messages = [
|
||||
m.add(messages, user_id="alice", metadata={"category": "movies"})
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
|
||||
const config = {
|
||||
llm: {
|
||||
provider: 'minimax',
|
||||
config: {
|
||||
apiKey: process.env.MINIMAX_API_KEY || '',
|
||||
model: 'MiniMax-M2.7',
|
||||
temperature: 0.2,
|
||||
maxTokens: 2000,
|
||||
topP: 1.0,
|
||||
},
|
||||
},
|
||||
};
|
||||
const memory = new Memory(config);
|
||||
const messages = [
|
||||
{ role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
|
||||
{ role: "assistant", content: "How about thriller movies? They can be quite engaging." },
|
||||
{ role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
|
||||
{ role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
|
||||
];
|
||||
await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
You can also configure the API base URL in the config:
|
||||
|
||||
```python
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
config = {
|
||||
"llm": {
|
||||
"provider": "minimax",
|
||||
@@ -51,6 +80,20 @@ config = {
|
||||
}
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
const config = {
|
||||
llm: {
|
||||
provider: 'minimax',
|
||||
config: {
|
||||
model: 'MiniMax-M2.7',
|
||||
baseURL: 'https://your-custom-endpoint.com',
|
||||
apiKey: 'your-api-key', // alternatively to using the environment variable
|
||||
},
|
||||
},
|
||||
};
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
## Config
|
||||
|
||||
All available parameters for the `minimax` config are present in [Master List of All Params in Config](../config).
|
||||
|
||||
@@ -1,226 +0,0 @@
|
||||
---
|
||||
title: LLM as Reranker
|
||||
description: "Use any LLM as a flexible reranker in Mem0 with custom prompts and domain-specific scoring logic."
|
||||
---
|
||||
|
||||
<Warning>
|
||||
**This page has been superseded.** Please see [LLM Reranker](/components/rerankers/models/llm_reranker) for the complete and up-to-date documentation on using LLMs for reranking.
|
||||
</Warning>
|
||||
|
||||
LLM-based reranker provides maximum flexibility by using any Large Language Model to score document relevance. This approach allows for custom prompts and domain-specific scoring logic.
|
||||
|
||||
## Supported LLM Providers
|
||||
|
||||
Any LLM provider supported by Mem0 can be used for reranking:
|
||||
|
||||
- **OpenAI**: GPT-4, GPT-3.5-turbo, etc.
|
||||
- **Anthropic**: Claude models
|
||||
- **Together**: Open-source models
|
||||
- **Groq**: Fast inference
|
||||
- **Ollama**: Local models
|
||||
- And more...
|
||||
|
||||
## Configuration
|
||||
|
||||
```python Python
|
||||
from mem0 import Memory
|
||||
|
||||
config = {
|
||||
"vector_store": {
|
||||
"provider": "chroma",
|
||||
"config": {
|
||||
"collection_name": "my_memories",
|
||||
"path": "./chroma_db"
|
||||
}
|
||||
},
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "gpt-4o-mini"
|
||||
}
|
||||
},
|
||||
"reranker": {
|
||||
"provider": "llm",
|
||||
"config": {
|
||||
"model": "gpt-4o-mini",
|
||||
"provider": "openai",
|
||||
"api_key": "your-openai-api-key", # or set OPENAI_API_KEY
|
||||
"top_k": 5,
|
||||
"temperature": 0.0
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
memory = Memory.from_config(config)
|
||||
```
|
||||
|
||||
## Custom Scoring Prompt
|
||||
|
||||
You can provide a custom prompt for relevance scoring:
|
||||
|
||||
```python Python
|
||||
custom_prompt = """You are a relevance scoring assistant. Rate how well this document answers the query.
|
||||
|
||||
Query: "{query}"
|
||||
Document: "{document}"
|
||||
|
||||
Score from 0.0 to 1.0 where:
|
||||
- 1.0: Perfect match, directly answers the query
|
||||
- 0.8-0.9: Highly relevant, good match
|
||||
- 0.6-0.7: Moderately relevant, partial match
|
||||
- 0.4-0.5: Slightly relevant, limited useful information
|
||||
- 0.0-0.3: Not relevant or no useful information
|
||||
|
||||
Provide only a single numerical score between 0.0 and 1.0."""
|
||||
|
||||
config["reranker"]["config"]["scoring_prompt"] = custom_prompt
|
||||
```
|
||||
|
||||
## Usage Example
|
||||
|
||||
```python Python
|
||||
import os
|
||||
from mem0 import Memory
|
||||
|
||||
# Set API key
|
||||
os.environ["OPENAI_API_KEY"] = "your-api-key"
|
||||
|
||||
# Initialize memory with LLM reranker
|
||||
config = {
|
||||
"vector_store": {"provider": "chroma"},
|
||||
"llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
|
||||
"reranker": {
|
||||
"provider": "llm",
|
||||
"config": {
|
||||
"model": "gpt-4o-mini",
|
||||
"provider": "openai",
|
||||
"temperature": 0.0
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
memory = Memory.from_config(config)
|
||||
|
||||
# Add memories
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm learning Python programming"},
|
||||
{"role": "user", "content": "I find object-oriented programming challenging"},
|
||||
{"role": "user", "content": "I love hiking in national parks"}
|
||||
]
|
||||
|
||||
memory.add(messages, user_id="david")
|
||||
|
||||
# Search with LLM reranking
|
||||
results = memory.search("What programming topics is the user studying?", filters={"user_id": "david"})
|
||||
|
||||
for result in results['results']:
|
||||
print(f"Memory: {result['memory']}")
|
||||
print(f"Vector Score: {result['score']:.3f}")
|
||||
print(f"Rerank Score: {result['rerank_score']:.3f}")
|
||||
print()
|
||||
```
|
||||
|
||||
```text Output
|
||||
Memory: I'm learning Python programming
|
||||
Vector Score: 0.856
|
||||
Rerank Score: 0.920
|
||||
|
||||
Memory: I find object-oriented programming challenging
|
||||
Vector Score: 0.782
|
||||
Rerank Score: 0.850
|
||||
```
|
||||
|
||||
## Domain-Specific Scoring
|
||||
|
||||
Create specialized scoring for your domain:
|
||||
|
||||
```python Python
|
||||
medical_prompt = """You are a medical relevance expert. Score how relevant this medical record is to the clinical query.
|
||||
|
||||
Clinical Query: "{query}"
|
||||
Medical Record: "{document}"
|
||||
|
||||
Consider:
|
||||
- Clinical relevance and accuracy
|
||||
- Patient safety implications
|
||||
- Diagnostic value
|
||||
- Treatment relevance
|
||||
|
||||
Score from 0.0 to 1.0. Provide only the numerical score."""
|
||||
|
||||
config = {
|
||||
"reranker": {
|
||||
"provider": "llm",
|
||||
"config": {
|
||||
"model": "gpt-4o-mini",
|
||||
"provider": "openai",
|
||||
"scoring_prompt": medical_prompt,
|
||||
"temperature": 0.0
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Multiple LLM Providers
|
||||
|
||||
Use different LLM providers for reranking:
|
||||
|
||||
```python Python
|
||||
# Using Anthropic Claude
|
||||
anthropic_config = {
|
||||
"reranker": {
|
||||
"provider": "llm",
|
||||
"config": {
|
||||
"model": "claude-3-haiku-20240307",
|
||||
"provider": "anthropic",
|
||||
"temperature": 0.0
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# Using local Ollama model
|
||||
ollama_config = {
|
||||
"reranker": {
|
||||
"provider": "llm",
|
||||
"config": {
|
||||
"model": "llama2:7b",
|
||||
"provider": "ollama",
|
||||
"temperature": 0.0
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Configuration Parameters
|
||||
|
||||
| Parameter | Description | Type | Default |
|
||||
|-----------|-------------|------|---------|
|
||||
| `model` | LLM model to use for scoring | `str` | `"gpt-4o-mini"` |
|
||||
| `provider` | LLM provider name | `str` | `"openai"` |
|
||||
| `api_key` | API key for the LLM provider | `str` | `None` |
|
||||
| `top_k` | Maximum documents to return | `int` | `None` |
|
||||
| `temperature` | Temperature for LLM generation | `float` | `0.0` |
|
||||
| `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
|
||||
| `scoring_prompt` | Custom prompt template | `str` | Default prompt |
|
||||
|
||||
## Advantages
|
||||
|
||||
- **Maximum Flexibility**: Custom prompts for any use case
|
||||
- **Domain Expertise**: Leverage LLM knowledge for specialized domains
|
||||
- **Interpretability**: Understand scoring through prompt engineering
|
||||
- **Multi-criteria**: Score based on multiple relevance factors
|
||||
|
||||
## Considerations
|
||||
|
||||
- **Latency**: Higher latency than specialized rerankers
|
||||
- **Cost**: LLM API costs per reranking operation
|
||||
- **Consistency**: May have slight variations in scoring
|
||||
- **Prompt Engineering**: Requires careful prompt design
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Temperature**: Use 0.0 for consistent scoring
|
||||
2. **Prompt Design**: Be specific about scoring criteria
|
||||
3. **Token Efficiency**: Keep prompts concise to reduce costs
|
||||
4. **Caching**: Cache results for repeated queries when possible
|
||||
5. **Fallback**: Handle API errors gracefully
|
||||
@@ -46,7 +46,7 @@ Here are the parameters available for configuring Baidu VectorDB:
|
||||
| `account` | Baidu VectorDB account name | `root` |
|
||||
| `api_key` | API key for accessing Baidu VectorDB | Required |
|
||||
| `database_name` | Name of the database | `mem0` |
|
||||
| `table_name` | Name of the table | `mem0_table` |
|
||||
| `table_name` | Name of the table | `mem0` |
|
||||
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
|
||||
| `metric_type` | Distance metric for similarity search | `L2` |
|
||||
|
||||
|
||||
@@ -56,6 +56,8 @@ Here are the parameters available for configuring Elasticsearch:
|
||||
| `api_key` | API key for authentication | `None` |
|
||||
| `user` | Username for basic authentication | `None` |
|
||||
| `password` | Password for basic authentication | `None` |
|
||||
| `use_ssl` | Whether to use SSL for the connection | `True` |
|
||||
| `ca_certs` | Path to CA bundle for SSL certificate verification | `None` |
|
||||
| `verify_certs` | Whether to verify SSL certificates | `True` |
|
||||
| `auto_create_index` | Whether to automatically create the index | `True` |
|
||||
| `custom_search_query` | Function returning a custom search query | `None` |
|
||||
|
||||
@@ -55,6 +55,7 @@ Here are the parameters available for configuring FAISS:
|
||||
| `path` | Path to store FAISS index and metadata | `/tmp/faiss/<collection_name>` |
|
||||
| `distance_strategy` | Distance metric strategy to use (options: 'euclidean', 'inner_product', 'cosine') | `euclidean` |
|
||||
| `normalize_L2` | Whether to normalize L2 vectors (only applicable for euclidean distance) | `False` |
|
||||
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
|
||||
|
||||
### Performance Considerations
|
||||
|
||||
|
||||
@@ -47,12 +47,12 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from "mem0ai";
|
||||
import { Memory } from "mem0ai/oss";
|
||||
import { OpenAIEmbeddings } from "@langchain/openai";
|
||||
import { MemoryVectorStore as LangchainMemoryStore } from "langchain/vectorstores/memory";
|
||||
import { MemoryVectorStore } from "langchain/vectorstores/memory";
|
||||
|
||||
const embeddings = new OpenAIEmbeddings();
|
||||
const vectorStore = new LangchainVectorStore(embeddings);
|
||||
const vectorStore = new MemoryVectorStore(embeddings);
|
||||
|
||||
const config = {
|
||||
"vector_store": {
|
||||
|
||||
@@ -42,8 +42,8 @@ Here are the parameters available for configuring MongoDB:
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| db_name | Name of the MongoDB database | `"mem0_db"` |
|
||||
| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
|
||||
| collection_name | Name of the MongoDB collection | `"mem0"` |
|
||||
| embedding_model_dims | Dimensions of the embedding vectors | `1536` |
|
||||
| mongo_uri | The MongoDB URI connection string | `mongodb://username:password@localhost:27017` |
|
||||
| mongo_uri | The MongoDB URI connection string | `mongodb://localhost:27017` |
|
||||
|
||||
> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://username:password@localhost:27017`.
|
||||
> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://localhost:27017`.
|
||||
|
||||
@@ -53,17 +53,14 @@ print(results)
|
||||
import "dotenv/config";
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const databaseUrl = new URL(process.env.DATABASE_URL!);
|
||||
|
||||
const m = new Memory({
|
||||
vectorStore: {
|
||||
provider: "pgvector",
|
||||
config: {
|
||||
user: decodeURIComponent(databaseUrl.username),
|
||||
password: decodeURIComponent(databaseUrl.password),
|
||||
host: databaseUrl.hostname,
|
||||
port: Number(databaseUrl.port || 5432),
|
||||
dbname: databaseUrl.pathname.slice(1) || "neondb",
|
||||
connectionString: process.env.DATABASE_URL!,
|
||||
ssl: {
|
||||
rejectUnauthorized: false,
|
||||
},
|
||||
collectionName: "memories",
|
||||
dimension: 1536,
|
||||
embeddingModelDims: 1536,
|
||||
@@ -90,6 +87,7 @@ const results = await m.search("What movies should I recommend?", {
|
||||
|
||||
console.log(results);
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
## SQL Migration
|
||||
@@ -116,20 +114,19 @@ DATABASE_URL=postgresql://user:password@ep-example.us-east-2.aws.neon.tech/neond
|
||||
| `sslmode` | PostgreSQL SSL mode. Use `require` for Neon. | Driver default |
|
||||
</Tab>
|
||||
<Tab title="TypeScript">
|
||||
The current Mem0 TypeScript `pgvector` adapter takes individual Postgres fields,
|
||||
so parse `DATABASE_URL` before creating `Memory`.
|
||||
Use the Neon `DATABASE_URL` directly with `connectionString`. Set `ssl` if your runtime needs an explicit TLS config object.
|
||||
|
||||
| Parameter | Description | Default |
|
||||
| -------------------- | ---------------------------------------------- | -------------- |
|
||||
| `connectionString` | Neon Postgres connection string. | Required |
|
||||
| `ssl` | Optional TLS settings passed directly to `pg`. | Driver default |
|
||||
| `collectionName` | Name for the vector collection. | `memories` |
|
||||
| `dimension` | Vector dimension for Mem0 config. | Auto-detected |
|
||||
| `embeddingModelDims` | Embedding model dimensions for table creation. | Required |
|
||||
| `hnsw` | Enables HNSW indexing. | `false` |
|
||||
|
||||
**TLS note:** `ssl: true` is sufficient for most Neon connections since Neon uses valid certificates. Use `ssl: { rejectUnauthorized: false }` only when connecting through Neon's connection pooler on certain edge runtimes (e.g. Cloudflare Workers) that require it, or when your environment does not trust the Neon CA chain.
|
||||
|
||||
| Parameter | Description | Default |
|
||||
| --- | --- | --- |
|
||||
| `user` | Database user. | Required |
|
||||
| `password` | Database password. | Required |
|
||||
| `host` | Database host. | Required |
|
||||
| `port` | Database port. | `5432` |
|
||||
| `dbname` | Database name. | `vector_store` |
|
||||
| `collectionName` | Name for the vector collection. | `memories` |
|
||||
| `dimension` | Vector dimension for Mem0 config. | Auto-detected |
|
||||
| `embeddingModelDims` | Embedding model dimensions for table creation. | Required |
|
||||
| `hnsw` | Enables HNSW indexing. | `false` |
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
|
||||
@@ -10,7 +10,7 @@ description: "Use AWS Neptune Analytics as a vector store in Mem0, combining gra
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install mem0ai[vector_stores]
|
||||
pip install mem0ai[vector-stores]
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
@@ -2,6 +2,7 @@
|
||||
title: "pgvector"
|
||||
description: "Use pgvector as a vector store in Mem0 for PostgreSQL-based vector similarity search with open-source simplicity."
|
||||
---
|
||||
|
||||
[pgvector](https://github.com/pgvector/pgvector) is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
|
||||
|
||||
### Usage
|
||||
@@ -21,7 +22,7 @@ config = {
|
||||
"password": "123",
|
||||
"host": "127.0.0.1",
|
||||
"port": "5432",
|
||||
}
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
@@ -30,25 +31,22 @@ messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
|
||||
]
|
||||
m.add(messages, user_id="alice", metadata={"category": "movies"})
|
||||
|
||||
```
|
||||
|
||||
```typescript TypeScript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const config = {
|
||||
vectorStore: {
|
||||
provider: 'pgvector',
|
||||
provider: "pgvector",
|
||||
config: {
|
||||
collectionName: 'memories',
|
||||
collectionName: "memories",
|
||||
embeddingModelDims: 1536,
|
||||
user: 'test',
|
||||
password: '123',
|
||||
host: '127.0.0.1',
|
||||
port: 5432,
|
||||
dbname: 'vector_store', // Optional; TypeScript OSS defaults to `vector_store` when omitted
|
||||
connectionString: "postgresql://test:123@localhost:5432/vector_store",
|
||||
diskann: false, // Optional, requires pgvectorscale extension
|
||||
hnsw: false, // Optional, for HNSW indexing
|
||||
},
|
||||
@@ -57,37 +55,44 @@ const config = {
|
||||
|
||||
const memory = new Memory(config);
|
||||
const messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
]
|
||||
{ role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
|
||||
{ role: "assistant", content: "How about thriller movies? They can be quite engaging." },
|
||||
{ role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
|
||||
{ role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
|
||||
];
|
||||
|
||||
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
### Config
|
||||
|
||||
Here are the parameters available for configuring pgvector:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `dbname` | The name of the database | `postgres` |
|
||||
| `collection_name` | The name of the collection | `mem0` |
|
||||
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
|
||||
| `user` | User name to connect to the database | `None` |
|
||||
| `password` | Password to connect to the database | `None` |
|
||||
| `host` | The host where the Postgres server is running | `None` |
|
||||
| `port` | The port where the Postgres server is running | `None` |
|
||||
| `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
|
||||
| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
|
||||
| `sslmode` | SSL mode for PostgreSQL connection (e.g., 'require', 'prefer', 'disable') | `None` |
|
||||
| `connection_string` | PostgreSQL connection string (overrides individual connection parameters) | `None` |
|
||||
| `connection_pool` | psycopg2 connection pool object (overrides connection string and individual parameters) | `None` |
|
||||
| Parameter | SDK | Description | Default Value |
|
||||
| -------------------- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
|
||||
| `connectionString` | TypeScript OSS | PostgreSQL connection string for direct connections. When set, Mem0 connects to the target database directly and skips the bootstrap `postgres` database flow. | `None` |
|
||||
| `ssl` | TypeScript OSS | SSL option passed directly to `pg`, either `true` or an SSL config object, for both `connectionString` and split-field connections. | `None` |
|
||||
| `dbname` | TypeScript OSS | Split-field database name. This is only used when `connectionString` is absent. | `vector_store` |
|
||||
| `collectionName` | TypeScript OSS | Collection name. | `memories` |
|
||||
| `embeddingModelDims` | TypeScript OSS | Dimensions of the embedding model. | Required |
|
||||
| `user` | TypeScript OSS + Python | Database user for split-field connections. | `None` |
|
||||
| `password` | TypeScript OSS + Python | Database password for split-field connections. | `None` |
|
||||
| `host` | TypeScript OSS + Python | Database host for split-field connections. | `None` |
|
||||
| `port` | TypeScript OSS + Python | Database port for split-field connections. | `None` |
|
||||
| `diskann` | TypeScript OSS + Python | Whether to use DiskANN for vector similarity search, requires pgvectorscale. | `False` |
|
||||
| `hnsw` | TypeScript OSS + Python | Whether to use HNSW for vector similarity search. | TypeScript OSS: `False`, Python: `True` |
|
||||
| `connection_string` | Python only | PostgreSQL connection string, overrides individual connection parameters. | `None` |
|
||||
| `sslmode` | Python only | SSL mode for PostgreSQL connections, such as `require`, `prefer`, or `disable`. | `None` |
|
||||
| `connection_pool` | Python only | psycopg connection pool object, overrides connection string and individual connection parameters. | `None` |
|
||||
|
||||
**Note (TypeScript OSS):** If you omit `dbname`, the TypeScript client uses the database name `vector_store`. Python defaults to `postgres` for `dbname`, as in the table above.
|
||||
**TypeScript OSS:** Use `connectionString` plus optional `ssl` for managed Postgres setups. If you omit `connectionString`, Mem0 falls back to split fields and uses `dbname`, `user`, `password`, `host`, `port`, and optional `ssl`.
|
||||
|
||||
**Python:** The Python SDK uses snake_case keys such as `connection_string`, `sslmode`, `collection_name`, and `embedding_model_dims`.
|
||||
|
||||
**Python connection priority**:
|
||||
|
||||
**Note**: The connection parameters have the following priority:
|
||||
1. `connection_pool` (highest priority)
|
||||
2. `connection_string`
|
||||
3. Individual connection parameters (`user`, `password`, `host`, `port`, `sslmode`)
|
||||
3. Individual connection parameters (`user`, `password`, `host`, `port`, `sslmode`)
|
||||
|
||||
@@ -18,7 +18,9 @@ os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
|
||||
config = {
|
||||
"vector_store": {
|
||||
"provider": "upstash_vector",
|
||||
"enable_embeddings": True,
|
||||
"config": {
|
||||
"enable_embeddings": True,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -9,7 +9,7 @@ description: "Use Valkey as an open-source vector store in Mem0 for high-perform
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install mem0ai[vector_stores]
|
||||
pip install mem0ai[vector-stores]
|
||||
```
|
||||
|
||||
## Usage
|
||||
@@ -51,7 +51,7 @@ Here are the parameters available for configuring Valkey:
|
||||
| `hnsw_ef_construction` | Size of dynamic candidate list for HNSW | `200` |
|
||||
| `hnsw_ef_runtime` | Size of dynamic candidate list for search | `10` |
|
||||
| `cluster_mode` | Enable cluster mode for Valkey cluster (CME) deployments | `false` |
|
||||
| `distance_metric` | Distance metric for vector similarity | `cosine` |
|
||||
| `timezone` | Timezone for timestamp handling | `UTC` |
|
||||
|
||||
## Cluster Mode
|
||||
|
||||
|
||||
@@ -24,7 +24,7 @@ config = {
|
||||
"deployment_index_id": "YOUR_DEPLOYMENT_INDEX_ID", # Required: Deployment-specific ID
|
||||
"project_id": "YOUR_PROJECT_ID", # Required: Google Cloud project ID
|
||||
"project_number": "YOUR_PROJECT_NUMBER", # Required: Google Cloud project number
|
||||
"region": "YOUR_REGION", # Optional: Defaults to GOOGLE_CLOUD_REGION
|
||||
"region": "YOUR_REGION", # Required: Google Cloud region
|
||||
"credentials_path": "path/to/credentials.json", # Optional: Defaults to GOOGLE_APPLICATION_CREDENTIALS
|
||||
"vector_search_api_endpoint": "YOUR_API_ENDPOINT" # Required for get operations
|
||||
}
|
||||
@@ -45,5 +45,6 @@ m.add("Your text here", user_id="user", metadata={"category": "example"})
|
||||
| `project_id` | Google Cloud project ID | Yes |
|
||||
| `project_number` | Google Cloud project number | Yes |
|
||||
| `vector_search_api_endpoint` | Vector search API endpoint | Yes (for get operations) |
|
||||
| `region` | Google Cloud region | No (defaults to GOOGLE_CLOUD_REGION) |
|
||||
| `region` | Google Cloud region | Yes |
|
||||
| `credentials_path` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) |
|
||||
| `service_account_json` | Service account credentials as a dictionary (alternative to `credentials_path`) | `None` |
|
||||
|
||||
@@ -7,7 +7,7 @@ description: "Use Weaviate as an open-source vector search engine in Mem0 for st
|
||||
|
||||
### Installation
|
||||
```bash
|
||||
pip install weaviate weaviate-client
|
||||
pip install weaviate-client
|
||||
```
|
||||
|
||||
### Usage
|
||||
@@ -48,4 +48,5 @@ Here are the parameters available for configuring Weaviate:
|
||||
| `collection_name` | The name of the collection to store the vectors | `mem0` |
|
||||
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
|
||||
| `cluster_url` | URL for the Weaviate server | `None` |
|
||||
| `auth_client_secret` | API key for Weaviate authentication | `None` |
|
||||
| `auth_client_secret` | API key for Weaviate authentication | `None` |
|
||||
| `additional_headers` | Additional headers to include in requests (`Dict[str, str]`) | `None` |
|
||||
@@ -10,7 +10,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
|
||||
See the list of supported vector databases below.
|
||||
|
||||
<Note>
|
||||
The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis, Valkey, Vectorize and in-memory vector database.
|
||||
The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, and an in-memory store.
|
||||
</Note>
|
||||
|
||||
<CardGroup cols={3}>
|
||||
|
||||
@@ -1,32 +1,65 @@
|
||||
---
|
||||
title: Development
|
||||
description: "Guide to contributing code to Mem0, covering the fork and clone workflow, PR submission, and code quality checks."
|
||||
description: "Guide to contributing code to Mem0, covering the issue-first workflow, the CLA, environment setup for the Python and TypeScript SDKs, and code quality checks."
|
||||
icon: "code"
|
||||
---
|
||||
|
||||
# Development Contributions
|
||||
|
||||
We strive to make contributions **easy, collaborative, and enjoyable**. Follow the steps below to ensure a smooth contribution process.
|
||||
We strive to make contributions **easy, collaborative, and enjoyable**. Mem0 is a
|
||||
polyglot monorepo containing the **Python SDK** (`mem0/`), the **TypeScript SDK**
|
||||
(`mem0-ts/`), CLIs, integrations, the self-hosted server, and the docs site.
|
||||
Follow the steps below for a smooth contribution process.
|
||||
|
||||
## Submitting Your Contribution through PR
|
||||
<Note>
|
||||
For the complete contributor checklist, see
|
||||
[CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md) in
|
||||
the repository root.
|
||||
</Note>
|
||||
|
||||
To contribute, follow these steps:
|
||||
## Before You Start
|
||||
|
||||
### 1. Open an Issue First
|
||||
|
||||
**Always open an issue before opening a pull request.** This lets us discuss the
|
||||
change, avoid duplicate work, and agree on the approach before you write code.
|
||||
|
||||
- Search [existing issues](https://github.com/mem0ai/mem0/issues) first.
|
||||
- If none match, open a
|
||||
[bug report](https://github.com/mem0ai/mem0/issues/new?template=bug_report.yml)
|
||||
or [feature request](https://github.com/mem0ai/mem0/issues/new?template=feature_request.yml).
|
||||
- For anything beyond a trivial fix, wait for a maintainer to confirm the approach.
|
||||
|
||||
Every pull request must link to an issue using `Closes #<issue-number>`.
|
||||
|
||||
### 2. Sign the Contributor License Agreement (CLA)
|
||||
|
||||
**We cannot merge any pull request until you have signed our Contributor License
|
||||
Agreement (CLA).** When you open your first PR, the CLA bot will comment with a
|
||||
link to sign — it takes less than a minute and only needs to be done once.
|
||||
|
||||
## Submitting Your Contribution through a PR
|
||||
|
||||
1. **Fork & Clone** the repository: [Mem0 on GitHub](https://github.com/mem0ai/mem0)
|
||||
2. **Create a Feature Branch**: Use a dedicated branch for your changes, e.g., `feature/my-new-feature`
|
||||
3. **Implement Changes**: If adding a feature or fixing a bug, ensure to:
|
||||
2. **Create a Feature Branch**: Use a dedicated branch, e.g., `feature/my-new-feature`
|
||||
3. **Implement Changes**: If adding a feature or fixing a bug, be sure to:
|
||||
- Write necessary **tests**
|
||||
- Add **documentation, docstrings, and runnable examples**
|
||||
4. **Code Quality Checks**:
|
||||
- Run **linting** to catch style issues
|
||||
- Ensure **all tests pass**
|
||||
5. **Submit a Pull Request**
|
||||
5. **Commit** using [Conventional Commits](https://www.conventionalcommits.org/)
|
||||
(`feat:`, `fix:`, `docs:`, `refactor:`, `test:`)
|
||||
6. **Submit a Pull Request** against `main`, linking the issue and filling out the
|
||||
PR template.
|
||||
|
||||
For detailed guidance on pull requests, refer to [GitHub's documentation](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
|
||||
|
||||
---
|
||||
|
||||
## Dependency Management
|
||||
## Python SDK (`mem0/`)
|
||||
|
||||
### Dependency Management
|
||||
|
||||
We use `hatch` as our package manager. Install it by following the [official instructions](https://hatch.pypa.io/latest/install/).
|
||||
|
||||
@@ -44,13 +77,9 @@ hatch -e dev_py_3_11 shell # For dev_py_3_11 (differences are mentioned in pypr
|
||||
make install_all
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Development Standards
|
||||
|
||||
### Pre-commit Hooks
|
||||
|
||||
Ensure `pre-commit` is installed before contributing:
|
||||
Ensure `pre-commit` is installed before contributing (hooks run ruff + isort):
|
||||
|
||||
```bash
|
||||
pre-commit install
|
||||
@@ -58,7 +87,7 @@ pre-commit install
|
||||
|
||||
### Linting with `ruff`
|
||||
|
||||
Run the linter and fix any reported issues before submitting your PR:
|
||||
Run the linter and fix any reported issues before submitting your PR (line length **120**):
|
||||
|
||||
```bash
|
||||
make lint
|
||||
@@ -66,10 +95,11 @@ make lint
|
||||
|
||||
### Code Formatting
|
||||
|
||||
To maintain a consistent code style, format your code:
|
||||
To maintain a consistent code style, format your code and sort imports (isort, `profile = "black"`):
|
||||
|
||||
```bash
|
||||
make format
|
||||
make sort
|
||||
```
|
||||
|
||||
### Testing with `pytest`
|
||||
@@ -84,10 +114,46 @@ make test
|
||||
|
||||
---
|
||||
|
||||
## Release Process
|
||||
## TypeScript SDK (`mem0-ts/`)
|
||||
|
||||
Currently, releases are handled manually. We aim for frequent releases, typically when new features or bug fixes are introduced.
|
||||
We use [`pnpm`](https://pnpm.io/) (v10+) for all TypeScript packages. **Do NOT use
|
||||
`npm` or `yarn`.**
|
||||
|
||||
```bash
|
||||
cd mem0-ts
|
||||
pnpm install
|
||||
|
||||
pnpm run build # tsup (CJS + ESM)
|
||||
pnpm run test # jest (all tests)
|
||||
pnpm run test:unit # unit tests with coverage
|
||||
```
|
||||
|
||||
### Standards
|
||||
|
||||
- **Build:** tsup
|
||||
- **Formatter:** Prettier
|
||||
- **Tests:** jest
|
||||
- Always run type checking after changes: `pnpm run typecheck` (or `tsc --noEmit`)
|
||||
- Use ES module `import` syntax — never `require()`
|
||||
|
||||
---
|
||||
|
||||
Thank you for contributing to Mem0!
|
||||
## Reporting Security Issues
|
||||
|
||||
**Do not report security vulnerabilities through public issues or pull requests.**
|
||||
Please follow our [Security Policy](https://github.com/mem0ai/mem0/blob/main/SECURITY.md)
|
||||
to report them privately.
|
||||
|
||||
---
|
||||
|
||||
## Release Process
|
||||
|
||||
Packages are published automatically via GitHub Actions when a GitHub Release is
|
||||
created with the correct tag prefix (e.g. `v*` for the Python SDK, `ts-v*` for the
|
||||
TypeScript SDK). See
|
||||
[CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md#releasing)
|
||||
for the full tag-prefix table and publishing details.
|
||||
|
||||
---
|
||||
|
||||
Thank you for contributing to Mem0!
|
||||
|
||||
+16
-3
@@ -123,8 +123,7 @@
|
||||
"icon": "arrow-right",
|
||||
"pages": [
|
||||
"migration/platform-v2-to-v3",
|
||||
"migration/oss-to-platform",
|
||||
"migration/api-changes"
|
||||
"migration/oss-to-platform"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -273,7 +272,8 @@
|
||||
"components/embedders/models/lmstudio",
|
||||
"components/embedders/models/together",
|
||||
"components/embedders/models/langchain",
|
||||
"components/embedders/models/aws_bedrock"
|
||||
"components/embedders/models/aws_bedrock",
|
||||
"components/embedders/models/fastembed"
|
||||
]
|
||||
}
|
||||
]
|
||||
@@ -533,6 +533,8 @@
|
||||
"api-reference/organization/get-org",
|
||||
"api-reference/organization/get-org-members",
|
||||
"api-reference/organization/add-org-member",
|
||||
"api-reference/organization/update-org-member",
|
||||
"api-reference/organization/remove-org-member",
|
||||
"api-reference/organization/delete-org"
|
||||
]
|
||||
},
|
||||
@@ -545,6 +547,9 @@
|
||||
"api-reference/project/get-project",
|
||||
"api-reference/project/get-project-members",
|
||||
"api-reference/project/add-project-member",
|
||||
"api-reference/project/update-project",
|
||||
"api-reference/project/update-project-member",
|
||||
"api-reference/project/remove-project-member",
|
||||
"api-reference/project/delete-project"
|
||||
]
|
||||
},
|
||||
@@ -624,6 +629,10 @@
|
||||
]
|
||||
},
|
||||
"redirects": [
|
||||
{
|
||||
"source": "/components/rerankers/models/llm",
|
||||
"destination": "/components/rerankers/models/llm_reranker"
|
||||
},
|
||||
{
|
||||
"source": "/migration/breaking-changes",
|
||||
"destination": "/"
|
||||
@@ -632,6 +641,10 @@
|
||||
"source": "/migration/v0-to-v1",
|
||||
"destination": "/"
|
||||
},
|
||||
{
|
||||
"source": "/migration/api-changes",
|
||||
"destination": "/migration/oss-v2-to-v3"
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/expiration-date",
|
||||
"destination": "/"
|
||||
|
||||
@@ -73,7 +73,7 @@ client = MemoryClient()
|
||||
# Define the agent
|
||||
agent = Agent(
|
||||
name="Personal Agent",
|
||||
model=OpenAIChat(id="gpt-4"),
|
||||
model=OpenAIChat(id="gpt-5-mini"),
|
||||
description="You are a helpful personal agent that helps me with day to day activities."
|
||||
"You can process both text and images.",
|
||||
markdown=True
|
||||
|
||||
@@ -43,7 +43,7 @@ OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
|
||||
memory_client = MemoryClient()
|
||||
agent = ConversableAgent(
|
||||
"chatbot",
|
||||
llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
|
||||
llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": OPENAI_API_KEY}]},
|
||||
code_execution_config=False,
|
||||
human_input_mode="NEVER",
|
||||
)
|
||||
@@ -99,7 +99,7 @@ For more complex scenarios, you can create multiple agents:
|
||||
manager = ConversableAgent(
|
||||
"manager",
|
||||
system_message="You are a manager who helps in resolving complex customer issues.",
|
||||
llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
|
||||
llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": OPENAI_API_KEY}]},
|
||||
human_input_mode="NEVER"
|
||||
)
|
||||
|
||||
|
||||
@@ -138,9 +138,10 @@ When installed via the plugin marketplace, Mem0 hooks into Claude Code's lifecyc
|
||||
|
||||
| Hook | Event | What it does |
|
||||
|------|-------|-------------|
|
||||
| **Setup** | `Setup` | Installs the mem0 SDK and dependencies (runs on init and maintenance) |
|
||||
| **Session start** | `SessionStart` | Loads prior memories and displays status banner |
|
||||
| **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message; skips short prompts |
|
||||
| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
|
||||
| **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
|
||||
| **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
|
||||
| **Stop** | `Stop` | Stores a session summary when the session ends |
|
||||
| **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |
|
||||
|
||||
@@ -123,7 +123,7 @@ When installed via the plugin marketplace, Mem0 hooks into Codex's lifecycle to
|
||||
|------|-------|-------------|
|
||||
| **Session start** | `SessionStart` | Loads prior memories and displays status banner |
|
||||
| **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
|
||||
| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
|
||||
| **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
|
||||
| **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
|
||||
| **Stop** | `Stop` | Stores a session summary when the session ends |
|
||||
| **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |
|
||||
|
||||
@@ -108,7 +108,7 @@ When installed via the Cursor Marketplace, Mem0 hooks into Cursor's lifecycle:
|
||||
|------|-------|-------------|
|
||||
| **Session start** | `sessionStart` | Loads prior memories and displays status banner |
|
||||
| **User prompt** | `beforeSubmitPrompt` | Searches relevant memories before each message; skips short prompts |
|
||||
| **Pre-tool (2 handlers)** | `preToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
|
||||
| **Pre-tool (3 handlers)** | `preToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
|
||||
| **Post-tool (2 handlers)** | `postToolUse` | Tracks stats, scans bash errors for related memories |
|
||||
| **Stop** | `stop` | Stores a session summary when the session ends |
|
||||
| **Pre-compact** | `preCompact` | Stores a summary before the context is compacted |
|
||||
|
||||
@@ -98,20 +98,9 @@ add_result = add_tool.invoke(add_input)
|
||||
|
||||
```json Output
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"memory": "Name is Alex",
|
||||
"event": "ADD"
|
||||
},
|
||||
{
|
||||
"memory": "Is a vegetarian",
|
||||
"event": "ADD"
|
||||
},
|
||||
{
|
||||
"memory": "Is allergic to nuts",
|
||||
"event": "ADD"
|
||||
}
|
||||
]
|
||||
"message": "Memory processing has been queued for background execution",
|
||||
"status": "PENDING",
|
||||
"event_id": "3a1b2c3d-4e5f-6789-abcd-ef0123456789"
|
||||
}
|
||||
```
|
||||
</CodeGroup>
|
||||
@@ -173,23 +162,25 @@ result = search_tool.invoke(search_input)
|
||||
```
|
||||
|
||||
```json Output
|
||||
[
|
||||
{
|
||||
"id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
|
||||
"memory": "Name is Alex",
|
||||
"user_id": "alex",
|
||||
"hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
|
||||
"metadata": {
|
||||
"food": "vegan"
|
||||
},
|
||||
"categories": [
|
||||
"personal_details"
|
||||
],
|
||||
"created_at": "2024-11-27T16:53:43.276872-08:00",
|
||||
"updated_at": "2024-11-27T16:53:43.276885-08:00",
|
||||
"score": 0.3810526501504994
|
||||
}
|
||||
]
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
|
||||
"memory": "Name is Alex",
|
||||
"user_id": "alex",
|
||||
"hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
|
||||
"metadata": {
|
||||
"food": "vegan"
|
||||
},
|
||||
"categories": [
|
||||
"personal_details"
|
||||
],
|
||||
"created_at": "2024-11-27T16:53:43.276872-08:00",
|
||||
"updated_at": "2024-11-27T16:53:43.276885-08:00",
|
||||
"score": 0.3810526501504994
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
|
||||
@@ -41,7 +41,7 @@ load_dotenv()
|
||||
# MEM0_API_KEY = 'your-mem0-key' # Replace with your actual Mem0 API key
|
||||
|
||||
# Initialize LangChain and Mem0
|
||||
llm = ChatOpenAI(model="gpt-4")
|
||||
llm = ChatOpenAI(model="gpt-5-mini")
|
||||
mem0 = MemoryClient()
|
||||
```
|
||||
|
||||
|
||||
@@ -121,7 +121,7 @@ async def websocket_endpoint(websocket: WebSocket):
|
||||
# LLM for response generation
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
model="gpt-3.5-turbo",
|
||||
model="gpt-5-mini",
|
||||
system_prompt="You are a helpful assistant that remembers past conversations."
|
||||
)
|
||||
|
||||
|
||||
@@ -26,12 +26,12 @@ Install the SDK provider and AI SDK:
|
||||
npm install @mem0/vercel-ai-provider ai@^6
|
||||
```
|
||||
|
||||
### Peer Dependencies
|
||||
### Dependencies
|
||||
|
||||
`@mem0/vercel-ai-provider` v3.0.0 requires:
|
||||
- `ai` v6+ (`^6.0.199`)
|
||||
- `@ai-sdk/provider` v3+ (`^3.0.10`)
|
||||
- Provider packages at v3+: `@ai-sdk/openai@^3`, `@ai-sdk/anthropic@^3`, `@ai-sdk/google@^3`, `@ai-sdk/groq@^3`, `@ai-sdk/cohere@^3`
|
||||
`@mem0/vercel-ai-provider` bundles `ai`, all `@ai-sdk/*` provider packages, and `@ai-sdk/provider` as regular dependencies — you do **not** need to install them separately. The install command above (`npm install @mem0/vercel-ai-provider ai@^6`) is sufficient.
|
||||
|
||||
The only true peer dependency is `zod` (optional):
|
||||
- `zod` v3+ (`^3.0.0`) — required only if you use Zod schemas in tool definitions
|
||||
|
||||
## Getting Started
|
||||
|
||||
@@ -305,6 +305,8 @@ These options can be passed per-request when creating a model instance:
|
||||
| `rerank` | `boolean` | Enable reranking of results |
|
||||
| `page` | `number` | Page number for pagination |
|
||||
| `page_size` | `number` | Results per page |
|
||||
| `mem0ApiKey` | `string` | Mem0 API key; overrides the `MEM0_API_KEY` env var |
|
||||
| `host` | `string` | Custom Mem0 API base URL for self-hosted deployments |
|
||||
|
||||
## Key Features
|
||||
|
||||
@@ -312,6 +314,7 @@ These options can be passed per-request when creating a model instance:
|
||||
- `retrieveMemories()`: Retrieves memory context for prompts as a formatted system prompt string.
|
||||
- `getMemories()`: Get memories from your profile in array format.
|
||||
- `addMemories()`: Adds user memories to enhance contextual responses.
|
||||
- `searchMemories()`: Searches memories and returns the raw results array (semantic search rather than the full retrieval pipeline).
|
||||
|
||||
## Migrating from v2.x
|
||||
|
||||
|
||||
+7
-4
@@ -228,7 +228,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
|
||||
- [OSS to Platform Migration](https://docs.mem0.ai/migration/oss-to-platform) [Both]: Use when moving from self-hosted to managed.
|
||||
- [OSS v2 to v3 Migration](https://docs.mem0.ai/migration/oss-v2-to-v3) [OSS]: Use when upgrading a self-hosted deployment across major versions.
|
||||
- [Platform v2 to v3 Migration](https://docs.mem0.ai/migration/platform-v2-to-v3) [Platform]: Use when upgrading a Platform integration across major versions.
|
||||
- [API Changes](https://docs.mem0.ai/migration/api-changes) [Both]: Use when the upgrade involves API surface changes.
|
||||
- [Server pgvector Image Upgrade](https://docs.mem0.ai/migration/server-pgvector-upgrade) [OSS]: Use when upgrading the self-hosted server Docker image from ankane/pgvector to pgvector/pgvector.
|
||||
- [Changelog](https://docs.mem0.ai/changelog/highlights) [Both]: Use when the user asks what shipped recently.
|
||||
|
||||
@@ -367,6 +366,8 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
|
||||
- [Get Organization](https://docs.mem0.ai/api-reference/organization/get-org) [Platform]: Use when fetching one org.
|
||||
- [Get Organization Members](https://docs.mem0.ai/api-reference/organization/get-org-members) [Platform]: Use when listing org members.
|
||||
- [Add Organization Member](https://docs.mem0.ai/api-reference/organization/add-org-member) [Platform]: Use when inviting a member to an org.
|
||||
- [Update Organization Member](https://docs.mem0.ai/api-reference/organization/update-org-member) [Platform]: Use when updating an org member's role.
|
||||
- [Remove Organization Member](https://docs.mem0.ai/api-reference/organization/remove-org-member) [Platform]: Use when removing a member from an organization.
|
||||
- [Delete Organization](https://docs.mem0.ai/api-reference/organization/delete-org) [Platform]: Use when removing an org.
|
||||
|
||||
### Projects
|
||||
@@ -375,6 +376,9 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
|
||||
- [Get Project](https://docs.mem0.ai/api-reference/project/get-project) [Platform]: Use when fetching one project.
|
||||
- [Get Project Members](https://docs.mem0.ai/api-reference/project/get-project-members) [Platform]: Use when listing project members.
|
||||
- [Add Project Member](https://docs.mem0.ai/api-reference/project/add-project-member) [Platform]: Use when inviting a member to a project.
|
||||
- [Update Project](https://docs.mem0.ai/api-reference/project/update-project) [Platform]: Use when updating project settings.
|
||||
- [Update Project Member](https://docs.mem0.ai/api-reference/project/update-project-member) [Platform]: Use when updating a project member's role.
|
||||
- [Remove Project Member](https://docs.mem0.ai/api-reference/project/remove-project-member) [Platform]: Use when removing a member from a project.
|
||||
- [Delete Project](https://docs.mem0.ai/api-reference/project/delete-project) [Platform]: Use when removing a project.
|
||||
|
||||
### Webhooks
|
||||
@@ -460,6 +464,7 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
|
||||
- [LM Studio Embeddings](https://docs.mem0.ai/components/embedders/models/lmstudio) [OSS]: Use when embeddings run through LM Studio.
|
||||
- [Together Embeddings](https://docs.mem0.ai/components/embedders/models/together) [OSS]: Use when embeddings run on Together.
|
||||
- [LangChain Embeddings](https://docs.mem0.ai/components/embedders/models/langchain) [OSS]: Use when embeddings are wrapped behind a LangChain adapter.
|
||||
- [FastEmbed](https://docs.mem0.ai/components/embedders/models/fastembed) [OSS]: Use when embeddings run locally via FastEmbed (ONNX).
|
||||
|
||||
### Vector Databases [OSS]
|
||||
- [Vector Database Overview](https://docs.mem0.ai/components/vectordbs/overview) [OSS]: Use when choosing a vector store.
|
||||
@@ -498,7 +503,5 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
|
||||
- [Custom Reranker Prompts](https://docs.mem0.ai/components/rerankers/custom-prompts) [OSS]: Use when rewriting reranker prompts.
|
||||
- [Cohere Reranker](https://docs.mem0.ai/components/rerankers/models/cohere) [OSS]: Use for Cohere Rerank.
|
||||
- [Sentence Transformer Reranker](https://docs.mem0.ai/components/rerankers/models/sentence_transformer) [OSS]: Use for local cross-encoder rerankers.
|
||||
- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.
|
||||
- [LLM Reranker (prompt)](https://docs.mem0.ai/components/rerankers/models/llm) [OSS]: Use when the reranker is a prompted LLM (config guide).
|
||||
- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
|
||||
- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
|
||||
- [Zero Entropy Reranker](https://docs.mem0.ai/components/rerankers/models/zero_entropy) [OSS]: Use for the Zero Entropy reranker.
|
||||
|
||||
@@ -1,564 +0,0 @@
|
||||
---
|
||||
title: API Reference Changes
|
||||
description: "Comprehensive reference of all API changes between Mem0 v0.x and v1.0.0 Beta, organized by component and method."
|
||||
icon: "code"
|
||||
iconType: "solid"
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
This page documents all API changes between Mem0 v0.x and v1.0.0 Beta, organized by component and method.
|
||||
|
||||
## Memory Class Changes
|
||||
|
||||
### Constructor
|
||||
|
||||
#### v0.x
|
||||
```python
|
||||
from mem0 import Memory
|
||||
|
||||
# Basic initialization
|
||||
m = Memory()
|
||||
|
||||
# With configuration
|
||||
config = {
|
||||
"version": "v1.0", # Supported in v0.x
|
||||
"vector_store": {...}
|
||||
}
|
||||
m = Memory.from_config(config)
|
||||
```
|
||||
|
||||
#### v1.0.0
|
||||
```python
|
||||
from mem0 import Memory
|
||||
|
||||
# Basic initialization (same)
|
||||
m = Memory()
|
||||
|
||||
# With configuration
|
||||
config = {
|
||||
"version": "v1.1", # v1.1+ only
|
||||
"vector_store": {...},
|
||||
# New optional features
|
||||
"reranker": {
|
||||
"provider": "cohere",
|
||||
"config": {...}
|
||||
}
|
||||
}
|
||||
m = Memory.from_config(config)
|
||||
```
|
||||
|
||||
### add() Method
|
||||
|
||||
#### v0.x Signature
|
||||
```python
|
||||
def add(
|
||||
self,
|
||||
messages,
|
||||
user_id: str = None,
|
||||
agent_id: str = None,
|
||||
run_id: str = None,
|
||||
metadata: dict = None,
|
||||
filters: dict = None,
|
||||
output_format: str = None, # ❌ REMOVED
|
||||
version: str = None # ❌ REMOVED
|
||||
) -> Union[List[dict], dict]:
|
||||
...
|
||||
```
|
||||
|
||||
#### v1.0.0 Signature
|
||||
```python
|
||||
def add(
|
||||
self,
|
||||
messages,
|
||||
*,
|
||||
user_id: str = None,
|
||||
agent_id: str = None,
|
||||
run_id: str = None,
|
||||
metadata: dict = None,
|
||||
infer: bool = True, # ✅ NEW: Control memory inference
|
||||
memory_type: str = None, # ✅ NEW: e.g. "procedural_memory"
|
||||
prompt: str = None # ✅ NEW: Custom extraction prompt
|
||||
) -> dict: # Always returns dict with "results" key
|
||||
...
|
||||
```
|
||||
|
||||
#### Changes Summary
|
||||
|
||||
| Parameter | v0.x | v1.0.0 | Change |
|
||||
|-----------|------|-----------|---------|
|
||||
| `messages` | ✅ | ✅ | Unchanged |
|
||||
| `user_id` | ✅ | ✅ | Unchanged |
|
||||
| `agent_id` | ✅ | ✅ | Unchanged |
|
||||
| `run_id` | ✅ | ✅ | Unchanged |
|
||||
| `metadata` | ✅ | ✅ | Unchanged |
|
||||
| `filters` | ✅ | ❌ | **REMOVED** — `add()` does not accept `filters` |
|
||||
| `output_format` | ✅ | ❌ | **REMOVED** |
|
||||
| `version` | ✅ | ❌ | **REMOVED** |
|
||||
| `infer` | ❌ | ✅ | **NEW** |
|
||||
| `memory_type` | ❌ | ✅ | **NEW** |
|
||||
| `prompt` | ❌ | ✅ | **NEW** |
|
||||
|
||||
#### Response Format Changes
|
||||
|
||||
**v0.x Response (variable format):**
|
||||
```python
|
||||
# With output_format="v1.0"
|
||||
[
|
||||
{
|
||||
"id": "mem_123",
|
||||
"memory": "User loves pizza",
|
||||
"event": "ADD"
|
||||
}
|
||||
]
|
||||
|
||||
# With output_format="v1.1"
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"id": "mem_123",
|
||||
"memory": "User loves pizza",
|
||||
"event": "ADD"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
**v1.0.0 Response (standardized):**
|
||||
```python
|
||||
# Always returns this format
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"id": "mem_123",
|
||||
"memory": "User loves pizza",
|
||||
"metadata": {...},
|
||||
"event": "ADD"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### search() Method
|
||||
|
||||
#### v0.x Signature
|
||||
```python
|
||||
def search(
|
||||
self,
|
||||
query: str,
|
||||
user_id: str = None,
|
||||
agent_id: str = None,
|
||||
run_id: str = None,
|
||||
limit: int = 100,
|
||||
filters: dict = None, # Basic key-value only
|
||||
output_format: str = None, # ❌ REMOVED
|
||||
version: str = None # ❌ REMOVED
|
||||
) -> Union[List[dict], dict]:
|
||||
...
|
||||
```
|
||||
|
||||
#### v1.0.0 Signature
|
||||
```python
|
||||
def search(
|
||||
self,
|
||||
query: str,
|
||||
*,
|
||||
top_k: int = 20, # ✅ Renamed from limit
|
||||
filters: dict = None, # ✅ ENHANCED: Advanced operators; must include user_id/agent_id/run_id
|
||||
threshold: float = 0.1, # ✅ NEW: Minimum score cutoff
|
||||
rerank: bool = False # ✅ NEW: Reranking support (default False)
|
||||
) -> dict: # Always returns dict with "results" key
|
||||
...
|
||||
```
|
||||
|
||||
<Warning>
|
||||
`user_id`, `agent_id`, and `run_id` are **not** top-level parameters in v1. They must be passed inside `filters`. Example: `m.search("query", filters={"user_id": "alice"})`.
|
||||
</Warning>
|
||||
|
||||
#### Enhanced Filtering
|
||||
|
||||
**v0.x Filters (basic):**
|
||||
```python
|
||||
# Simple key-value filtering only
|
||||
filters = {
|
||||
"category": "food",
|
||||
"user_id": "alice"
|
||||
}
|
||||
```
|
||||
|
||||
**v1.0.0 Filters (enhanced):**
|
||||
```python
|
||||
# Advanced filtering with operators
|
||||
filters = {
|
||||
"AND": [
|
||||
{"category": "food"},
|
||||
{"score": {"gte": 0.8}},
|
||||
{
|
||||
"OR": [
|
||||
{"priority": "high"},
|
||||
{"urgent": True}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# Comparison operators
|
||||
filters = {
|
||||
"score": {"gt": 0.5}, # Greater than
|
||||
"priority": {"gte": 5}, # Greater than or equal
|
||||
"rating": {"lt": 3}, # Less than
|
||||
"confidence": {"lte": 0.9}, # Less than or equal
|
||||
"status": {"eq": "active"}, # Equal
|
||||
"archived": {"ne": True}, # Not equal
|
||||
"tags": {"in": ["work", "personal"]}, # In list
|
||||
"category": {"nin": ["spam", "deleted"]} # Not in list
|
||||
}
|
||||
```
|
||||
|
||||
### get_all() Method
|
||||
|
||||
#### v0.x Signature
|
||||
```python
|
||||
def get_all(
|
||||
self,
|
||||
user_id: str = None,
|
||||
agent_id: str = None,
|
||||
run_id: str = None,
|
||||
filters: dict = None,
|
||||
output_format: str = None, # ❌ REMOVED
|
||||
version: str = None # ❌ REMOVED
|
||||
) -> Union[List[dict], dict]:
|
||||
...
|
||||
```
|
||||
|
||||
#### v1.0.0 Signature
|
||||
```python
|
||||
def get_all(
|
||||
self,
|
||||
user_id: str = None,
|
||||
agent_id: str = None,
|
||||
run_id: str = None,
|
||||
filters: dict = None # ✅ ENHANCED: Advanced operators
|
||||
) -> dict: # Always returns dict with "results" key
|
||||
...
|
||||
```
|
||||
|
||||
### update() Method
|
||||
|
||||
#### No Breaking Changes
|
||||
```python
|
||||
# Same signature in both versions
|
||||
def update(
|
||||
self,
|
||||
memory_id: str,
|
||||
data: str
|
||||
) -> dict:
|
||||
...
|
||||
```
|
||||
|
||||
### delete() Method
|
||||
|
||||
#### No Breaking Changes
|
||||
```python
|
||||
# Same signature in both versions
|
||||
def delete(
|
||||
self,
|
||||
memory_id: str
|
||||
) -> dict:
|
||||
...
|
||||
```
|
||||
|
||||
### delete_all() Method
|
||||
|
||||
#### Breaking Change — Empty filter no longer silently deletes everything
|
||||
|
||||
**Before:** calling `delete_all()` with no filters silently deleted **all memories in the project**.
|
||||
|
||||
**After:**
|
||||
- No filters → raises a validation error (prevents accidental full-project wipe).
|
||||
- Concrete ID (e.g. `user_id="alice"`) → deletes memories for that entity (unchanged).
|
||||
- `"*"` for a filter → deletes all memories for that entity type across the project (new).
|
||||
- All four filters set to `"*"` → explicit full project wipe (new, requires opt-in on every parameter).
|
||||
|
||||
This change replaces the silent full-project delete (triggered by an empty or missing filter) with a validation error, and introduces `"*"` wildcards as the intentional path for bulk deletion.
|
||||
|
||||
```python
|
||||
# v0.x — no filter silently wiped all project memories
|
||||
m.delete_all() # DANGER: deleted everything
|
||||
m.delete_all(user_id="alice") # deleted alice's memories
|
||||
|
||||
# v1.x — no filter now raises an error; use "*" for intentional bulk deletes
|
||||
m.delete_all() # ERROR: at least one filter required
|
||||
m.delete_all(user_id="alice") # unchanged
|
||||
m.delete_all(user_id="*") # NEW — delete all users' memories
|
||||
m.delete_all(user_id="*", agent_id="*", app_id="*", run_id="*") # NEW — full project wipe
|
||||
```
|
||||
|
||||
## Platform Client (MemoryClient) Changes
|
||||
|
||||
### async_mode Removed
|
||||
|
||||
<Note>
|
||||
`async_mode` was removed in v3. Remove any `async_mode=` argument from your `MemoryClient` calls.
|
||||
</Note>
|
||||
|
||||
## Configuration Changes
|
||||
|
||||
### Memory Configuration
|
||||
|
||||
#### v0.x Config Options
|
||||
```python
|
||||
config = {
|
||||
"vector_store": {...},
|
||||
"llm": {...},
|
||||
"embedder": {...},
|
||||
"version": "v1.0", # ❌ v1.0 no longer supported
|
||||
"history_db_path": "...",
|
||||
"custom_instructions": "..."
|
||||
}
|
||||
```
|
||||
|
||||
#### v1.0.0 Config Options
|
||||
```python
|
||||
config = {
|
||||
"vector_store": {...},
|
||||
"llm": {...},
|
||||
"embedder": {...},
|
||||
"reranker": { # ✅ NEW: Reranker support
|
||||
"provider": "cohere",
|
||||
"config": {...}
|
||||
},
|
||||
"version": "v1.1", # ✅ v1.1+ only
|
||||
"history_db_path": "...",
|
||||
"custom_instructions": "..." # ✅ Use this for custom extraction instructions
|
||||
}
|
||||
```
|
||||
|
||||
<Note>
|
||||
`graph_store` is no longer a top-level `MemoryConfig` field. Graph support is configured separately.
|
||||
`custom_update_memory_prompt` has been removed — use `custom_instructions` instead.
|
||||
</Note>
|
||||
|
||||
### New Configuration Options
|
||||
|
||||
#### Reranker Configuration
|
||||
```text
|
||||
# Cohere reranker
|
||||
"reranker": {
|
||||
"provider": "cohere",
|
||||
"config": {
|
||||
"model": "rerank-english-v3.0",
|
||||
"api_key": "your-api-key",
|
||||
"top_k": 10
|
||||
}
|
||||
}
|
||||
|
||||
# Sentence Transformer reranker
|
||||
"reranker": {
|
||||
"provider": "sentence_transformer",
|
||||
"config": {
|
||||
"model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
|
||||
"device": "cuda"
|
||||
}
|
||||
}
|
||||
|
||||
# Hugging Face reranker
|
||||
"reranker": {
|
||||
"provider": "huggingface",
|
||||
"config": {
|
||||
"model": "BAAI/bge-reranker-base",
|
||||
"device": "cuda"
|
||||
}
|
||||
}
|
||||
|
||||
# LLM-based reranker
|
||||
"reranker": {
|
||||
"provider": "llm_reranker",
|
||||
"config": {
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "gpt-4",
|
||||
"api_key": "your-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Error Handling Changes
|
||||
|
||||
### New Error Types
|
||||
|
||||
#### v0.x Errors
|
||||
```python
|
||||
# Generic exceptions
|
||||
try:
|
||||
result = m.add("content", user_id="alice", version="v1.0")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
```
|
||||
|
||||
#### v1.0.0 Errors
|
||||
```python
|
||||
# More specific error handling
|
||||
try:
|
||||
result = m.add("content", user_id="alice")
|
||||
except ValueError as e:
|
||||
if "v1.0 API format is no longer supported" in str(e):
|
||||
# Handle version compatibility error
|
||||
pass
|
||||
elif "Invalid filter operator" in str(e):
|
||||
# Handle filter syntax error
|
||||
pass
|
||||
except TypeError as e:
|
||||
# Handle parameter errors
|
||||
pass
|
||||
except Exception as e:
|
||||
# Handle unexpected errors
|
||||
pass
|
||||
```
|
||||
|
||||
### Validation Changes
|
||||
|
||||
#### Stricter Parameter Validation
|
||||
|
||||
**v0.x (Lenient):**
|
||||
```python
|
||||
# Unknown parameters might be ignored
|
||||
result = m.add("content", user_id="alice", unknown_param="value")
|
||||
```
|
||||
|
||||
**v1.0.0 (Strict):**
|
||||
```python
|
||||
# Unknown parameters raise TypeError
|
||||
try:
|
||||
result = m.add("content", user_id="alice", unknown_param="value")
|
||||
except TypeError as e:
|
||||
print(f"Invalid parameter: {e}")
|
||||
```
|
||||
|
||||
## Response Schema Changes
|
||||
|
||||
### Memory Object Schema
|
||||
|
||||
#### v0.x Schema
|
||||
```python
|
||||
{
|
||||
"id": "mem_123",
|
||||
"memory": "User loves pizza",
|
||||
"user_id": "alice",
|
||||
"metadata": {...},
|
||||
"created_at": "2024-01-01T00:00:00Z",
|
||||
"updated_at": "2024-01-01T00:00:00Z",
|
||||
"score": 0.95 # In search results
|
||||
}
|
||||
```
|
||||
|
||||
#### v1.0.0 Schema (Enhanced)
|
||||
```python
|
||||
{
|
||||
"id": "mem_123",
|
||||
"memory": "User loves pizza",
|
||||
"user_id": "alice",
|
||||
"agent_id": "assistant", # ✅ More context
|
||||
"run_id": "session_001", # ✅ More context
|
||||
"metadata": {...},
|
||||
"categories": ["food"], # ✅ NEW: Auto-categorization
|
||||
"immutable": false, # ✅ NEW: Immutability flag
|
||||
"created_at": "2024-01-01T00:00:00Z",
|
||||
"updated_at": "2024-01-01T00:00:00Z",
|
||||
"score": 0.95, # In search results
|
||||
"rerank_score": 0.98 # ✅ NEW: If reranking used
|
||||
}
|
||||
```
|
||||
|
||||
## Migration Code Examples
|
||||
|
||||
### Simple Migration
|
||||
|
||||
#### Before (v0.x)
|
||||
```python
|
||||
from mem0 import Memory
|
||||
|
||||
m = Memory()
|
||||
|
||||
# Add with deprecated parameters
|
||||
result = m.add(
|
||||
"I love pizza",
|
||||
user_id="alice",
|
||||
output_format="v1.1",
|
||||
version="v1.0"
|
||||
)
|
||||
|
||||
# Handle variable response format
|
||||
if isinstance(result, list):
|
||||
memories = result
|
||||
else:
|
||||
memories = result.get("results", [])
|
||||
|
||||
for memory in memories:
|
||||
print(memory["memory"])
|
||||
```
|
||||
|
||||
#### After (v1.0.0 )
|
||||
```python
|
||||
from mem0 import Memory
|
||||
|
||||
m = Memory()
|
||||
|
||||
# Add without deprecated parameters
|
||||
result = m.add(
|
||||
"I love pizza",
|
||||
user_id="alice"
|
||||
)
|
||||
|
||||
# Always dict format with "results" key
|
||||
for memory in result["results"]:
|
||||
print(memory["memory"])
|
||||
```
|
||||
|
||||
### Advanced Migration
|
||||
|
||||
#### Before (v0.x)
|
||||
```python
|
||||
# Basic filtering
|
||||
results = m.search(
|
||||
"food preferences",
|
||||
user_id="alice",
|
||||
filters={"category": "food"},
|
||||
output_format="v1.1"
|
||||
)
|
||||
```
|
||||
|
||||
#### After (v1.0.0 )
|
||||
```python
|
||||
# Enhanced filtering with reranking
|
||||
results = m.search(
|
||||
"food preferences",
|
||||
user_id="alice",
|
||||
filters={
|
||||
"AND": [
|
||||
{"category": "food"},
|
||||
{"score": {"gte": 0.8}}
|
||||
]
|
||||
},
|
||||
rerank=True
|
||||
)
|
||||
```
|
||||
|
||||
## Summary
|
||||
|
||||
| Component | v0.x | v1.0.0 | Status |
|
||||
|-----------|------|-----------|---------|
|
||||
| `add()` method | Variable response | Standardized response | ⚠️ Breaking |
|
||||
| `search()` method | Basic filtering | Enhanced filtering + reranking | ⚠️ Breaking |
|
||||
| `get_all()` method | Variable response | Standardized response | ⚠️ Breaking |
|
||||
| Response format | Variable | Always `{"results": [...]}` | ⚠️ Breaking |
|
||||
| Reranking | ❌ Not available | ✅ Full support | ✅ New feature |
|
||||
| Advanced filtering | ❌ Basic only | ✅ Full operators | ✅ Enhancement |
|
||||
| Error handling | Generic | Specific error types | ✅ Improvement |
|
||||
|
||||
<Info>
|
||||
Use this reference to systematically update your codebase. Test each change thoroughly before deploying to production.
|
||||
</Info>
|
||||
@@ -62,7 +62,7 @@ These changes produce a **+20 point improvement on LoCoMo** (71.4 → 91.6) and
|
||||
|---|---|---|---|
|
||||
| Constructor | `MemoryClient(api_key, org_id, project_id)` | `MemoryClient(api_key)` | Remove `org_id`, `project_id` from constructor |
|
||||
| Method options | `client.add(messages, **kwargs)` | `client.add(messages, options=AddMemoryOptions(...))` | Use typed option classes (or `**kwargs` still works) |
|
||||
| Removed params | `api_version`, `output_format`, `async_mode`, `filter_memories`, `expiration_date`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `enable_graph`, `org_name`, `project_name` | — | Remove from all calls |
|
||||
| Removed params | `api_version`, `output_format`, `async_mode`, `filter_memories`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `enable_graph`, `org_name`, `project_name` | — | Remove from all calls |
|
||||
|
||||
### TypeScript Client SDK
|
||||
|
||||
@@ -70,7 +70,7 @@ These changes produce a **+20 point improvement on LoCoMo** (71.4 → 91.6) and
|
||||
|---|---|---|---|
|
||||
| Constructor | `new MemoryClient({ apiKey, organizationId, projectId })` | `new MemoryClient({ apiKey })` | Remove `organizationId`, `projectId`, `organizationName`, `projectName` |
|
||||
| All params | snake_case: `user_id`, `agent_id`, `top_k` | camelCase: `userId`, `agentId`, `topK` | Rename all params to camelCase |
|
||||
| Removed params | `api_version`, `output_format`, `async_mode`, `enable_graph`, `org_id`, `project_id`, `org_name`, `project_name`, `filter_memories`, `batch_size`, `force_add_only`, `immutable`, `expiration_date`, `includes`, `excludes`, `keyword_search` | — | Remove from all calls |
|
||||
| Removed params | `api_version`, `output_format`, `async_mode`, `enable_graph`, `org_id`, `project_id`, `org_name`, `project_name`, `filter_memories`, `batch_size`, `force_add_only`, `immutable`, `includes`, `excludes`, `keyword_search` | — | Remove from all calls |
|
||||
| Output format enum | `OutputFormat.V1`, `OutputFormat.V1_1` | Removed | v1.1 is now always used |
|
||||
| API version enum | `API_VERSION.V1`, `API_VERSION.V2` | Removed | Handled internally |
|
||||
|
||||
@@ -423,7 +423,7 @@ These parameters have been removed across all SDKs. Remove them from your code:
|
||||
|
||||
**All methods:** `api_version`, `output_format`, `async_mode`, `org_name`, `project_name`, `org_id`, `project_id`
|
||||
|
||||
**add():** `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
|
||||
**add():** `enable_graph`, `immutable`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
|
||||
|
||||
**search():** `enable_graph`
|
||||
|
||||
@@ -437,7 +437,7 @@ These parameters have been removed across all SDKs. Remove them from your code:
|
||||
|
||||
**All methods:** `OutputFormat` enum, `API_VERSION` enum
|
||||
|
||||
**add():** `enable_graph` / `enableGraph`, `async_mode` / `asyncMode`, `output_format` / `outputFormat`, `immutable`, `expiration_date` / `expirationDate`, `filter_memories` / `filterMemories`, `batch_size` / `batchSize`, `force_add_only` / `forceAddOnly`, `includes`, `excludes`, `keyword_search` / `keywordSearch`
|
||||
**add():** `enable_graph` / `enableGraph`, `async_mode` / `asyncMode`, `output_format` / `outputFormat`, `immutable`, `filter_memories` / `filterMemories`, `batch_size` / `batchSize`, `force_add_only` / `forceAddOnly`, `includes`, `excludes`, `keyword_search` / `keywordSearch`
|
||||
|
||||
**search():** `enable_graph` / `enableGraph`
|
||||
|
||||
|
||||
@@ -215,7 +215,7 @@ client.add(messages, user_id="alice")
|
||||
# async_mode and output_format removed (async by default, v1.1 always)
|
||||
```
|
||||
|
||||
**Removed parameters:** `org_id`, `project_id`, `api_version`, `output_format`, `async_mode`, `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`, `org_name`, `project_name`
|
||||
**Removed parameters:** `org_id`, `project_id`, `api_version`, `output_format`, `async_mode`, `enable_graph`, `immutable`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`, `org_name`, `project_name`
|
||||
|
||||
### TypeScript Client SDK
|
||||
|
||||
@@ -242,7 +242,7 @@ await client.search("query", {
|
||||
});
|
||||
```
|
||||
|
||||
**Removed:** `OutputFormat` enum, `API_VERSION` enum, `organizationId`, `projectId`, `organizationName`, `projectName`, `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `expirationDate`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
|
||||
**Removed:** `OutputFormat` enum, `API_VERSION` enum, `organizationId`, `projectId`, `organizationName`, `projectName`, `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
|
||||
|
||||
<Info>
|
||||
For the full list of parameter changes across all SDKs, see the [OSS migration guide](/migration/oss-v2-to-v3#removed-parameters-reference).
|
||||
|
||||
@@ -130,7 +130,7 @@ memory = Memory.from_config_file("config.yaml")
|
||||
</Tabs>
|
||||
|
||||
<Info icon="check">
|
||||
Run `memory.add(["Remember my favorite cafe in Tokyo."], user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
|
||||
Run `memory.add("Remember my favorite cafe in Tokyo.", user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
|
||||
</Info>
|
||||
|
||||
## Tune component settings
|
||||
|
||||
@@ -18,7 +18,7 @@ icon: "bolt"
|
||||
</Warning>
|
||||
|
||||
<Note>
|
||||
Working in TypeScript? The Node SDK still uses synchronous calls—use `Memory` there and rely on Python’s `AsyncMemory` when you need awaited operations.
|
||||
Working in TypeScript? The OSS `Memory` class in the Node SDK (`mem0ai/oss`) is also fully async — every method returns a `Promise` and must be `await`ed. Python’s `AsyncMemory` serves the same purpose within Python async frameworks like FastAPI. Both runtimes support awaited memory operations; choose the SDK that matches your language.
|
||||
</Note>
|
||||
|
||||
## Feature anatomy
|
||||
|
||||
@@ -165,8 +165,7 @@ await memory.add("Yesterday, I ordered a laptop, the order id is 12345", { userI
|
||||
{"memory": "Ordered a laptop", "event": "ADD"},
|
||||
{"memory": "Order ID: 12345", "event": "ADD"},
|
||||
{"memory": "Order placed yesterday", "event": "ADD"}
|
||||
],
|
||||
"relations": []
|
||||
]
|
||||
}
|
||||
```
|
||||
</CodeGroup>
|
||||
@@ -188,8 +187,7 @@ await memory.add("I like going to hikes", { userId: "user123" });
|
||||
|
||||
```json Output
|
||||
{
|
||||
"results": [],
|
||||
"relations": []
|
||||
"results": []
|
||||
}
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
@@ -41,6 +41,14 @@ Multimodal support lets Mem0 extract facts from images alongside regular text. A
|
||||
|
||||
## Configure it
|
||||
|
||||
<Warning>
|
||||
You must set `enable_vision: True` in your LLM config for image content to be processed. Without it, image turns are silently dropped and no vision memories are created. Example:
|
||||
```python
|
||||
config = {"llm": {"provider": "openai", "config": {"enable_vision": True, "vision_details": "auto"}}}
|
||||
client = Memory.from_config(config)
|
||||
```
|
||||
</Warning>
|
||||
|
||||
### Add image messages from URLs
|
||||
|
||||
<CodeGroup>
|
||||
@@ -66,7 +74,7 @@ client.add(messages, user_id="alice")
|
||||
```
|
||||
|
||||
```ts TypeScript
|
||||
import { Memory } from "mem0ai";
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const client = new Memory();
|
||||
|
||||
@@ -123,7 +131,7 @@ client.add(messages, user_id="alice")
|
||||
|
||||
```ts TypeScript
|
||||
import fs from "fs";
|
||||
import { Memory } from "mem0ai";
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
function encodeImage(imagePath: string) {
|
||||
const buffer = fs.readFileSync(imagePath);
|
||||
@@ -226,7 +234,7 @@ client.add(messages, user_id="user123")
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
from mem0 import Memory
|
||||
from mem0.exceptions import InvalidImageError, FileSizeError
|
||||
from mem0.exceptions import ValidationError
|
||||
|
||||
client = Memory()
|
||||
|
||||
@@ -242,16 +250,14 @@ try:
|
||||
client.add(messages, user_id="user123")
|
||||
print("Image processed successfully")
|
||||
|
||||
except InvalidImageError:
|
||||
print("Invalid image format or corrupted file")
|
||||
except FileSizeError:
|
||||
print("Image file too large")
|
||||
except ValidationError as exc:
|
||||
print(f"Image validation error: {exc}")
|
||||
except Exception as exc:
|
||||
print(f"Unexpected error: {exc}")
|
||||
```
|
||||
|
||||
```ts TypeScript
|
||||
import { Memory } from "mem0ai";
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const client = new Memory();
|
||||
|
||||
|
||||
@@ -124,7 +124,7 @@ config = {
|
||||
"provider": "llm_reranker",
|
||||
"config": {
|
||||
"provider": "openai",
|
||||
"model": "gpt-4o-mini",
|
||||
"model": "gpt-5-mini",
|
||||
"api_key": "your-openai-api-key",
|
||||
"top_k": 5
|
||||
}
|
||||
@@ -150,7 +150,7 @@ config = {
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "gpt-4",
|
||||
"model": "gpt-5-mini",
|
||||
"api_key": "your-openai-api-key"
|
||||
}
|
||||
},
|
||||
|
||||
+43
-8
@@ -1779,6 +1779,11 @@
|
||||
"type": "object",
|
||||
"description": "Entity and metadata filters. Must include at least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`).",
|
||||
"additionalProperties": true
|
||||
},
|
||||
"show_expired": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "When true, include memories whose `expiration_date` has passed. Expired memories are hidden by default."
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -1977,6 +1982,12 @@
|
||||
"additionalProperties": true,
|
||||
"description": "User-supplied metadata to attach to each extracted memory."
|
||||
},
|
||||
"expiration_date": {
|
||||
"type": "string",
|
||||
"format": "date",
|
||||
"nullable": true,
|
||||
"description": "Optional expiration date in YYYY-MM-DD format. After this date, memories are hidden from search and get-all unless `show_expired` is true."
|
||||
},
|
||||
"custom_instructions": {
|
||||
"type": "string",
|
||||
"description": "Project-level instructions that guide extraction for this call."
|
||||
@@ -2094,6 +2105,11 @@
|
||||
"description": "Entity and metadata filters. Must include at least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`). Supports `AND`, `OR`, `NOT`, and comparison operators (`in`, `gte`, `lte`, `gt`, `lt`, `contains`, `icontains`, `ne`).",
|
||||
"additionalProperties": true
|
||||
},
|
||||
"show_expired": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "When true, include memories whose `expiration_date` has passed. Expired memories are hidden by default."
|
||||
},
|
||||
"top_k": {
|
||||
"type": "integer",
|
||||
"minimum": 1,
|
||||
@@ -2432,6 +2448,12 @@
|
||||
"metadata": {
|
||||
"type": "object",
|
||||
"description": "Additional metadata associated with the memory"
|
||||
},
|
||||
"expiration_date": {
|
||||
"type": "string",
|
||||
"format": "date",
|
||||
"nullable": true,
|
||||
"description": "Expiration date in YYYY-MM-DD format, or null to clear the expiration date."
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -4861,8 +4883,7 @@
|
||||
"items": {
|
||||
"type": "object",
|
||||
"required": [
|
||||
"memory_id",
|
||||
"text"
|
||||
"memory_id"
|
||||
],
|
||||
"properties": {
|
||||
"memory_id": {
|
||||
@@ -4873,6 +4894,11 @@
|
||||
"text": {
|
||||
"type": "string",
|
||||
"description": "The new text content for the memory"
|
||||
},
|
||||
"metadata": {
|
||||
"type": "object",
|
||||
"additionalProperties": true,
|
||||
"description": "Updated metadata to associate with the memory."
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -4948,18 +4974,27 @@
|
||||
"schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"memory_ids": {
|
||||
"memories": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string",
|
||||
"format": "uuid"
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"memory_id": {
|
||||
"type": "string",
|
||||
"format": "uuid",
|
||||
"description": "The unique identifier of the memory to delete."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"memory_id"
|
||||
]
|
||||
},
|
||||
"maxItems": 1000,
|
||||
"description": "Array of memory IDs to delete."
|
||||
"description": "Array of memory objects to delete."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"memory_ids"
|
||||
"memories"
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -6256,4 +6291,4 @@
|
||||
}
|
||||
},
|
||||
"x-original-swagger-version": "2.0"
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user